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Applied Geography 27 (2007) 89–111

The relationship between place attachment andlandscape values: Toward mapping place attachment

Gregory Browna,b,�, Christopher Raymondb

aNatural Resource Management, Green Mountain College, Poultney, VT 05764, USAbSchool of Natural and Built Environments, University of South Australia, Mawson Lakes, South Australia, 5095

Abstract

This paper examines the relationships between place attachment and landscape values using two

measures of place attachment—a psychometric, scale-based measure [Williams, D. R., & Vaske, J. J.

(2003). The measurement of place attachment: Validity and generalisability of a psychometric

approach. Forest Science, 49(6), 830–840] and a map-based measure derived from mapped special

places [Brown, G. (2005). Mapping spatial attributes in survey research for natural resource

management: Methods and applications. Society and Natural Resources, 18(1), 17–39]. We first

examine the external validity of a two-dimensional, psychometric place attachment scale in Australia

and its relationship with place-based landscape values. The place attachment scale and landscape

value measures were included in a mail survey of residents and visitors to the Otways region

(Victoria, Australia). Exploratory factor analysis of resident subgroups and visitors demonstrate the

place attachment scale consists of two dimensions with high reliability. We use regression analysis to

show that landscape importance values, especially spiritual and wilderness values, are significant

predictors of the scale-based measure of place attachment. We then examine the relationship between

a map-based measure of place attachment and landscape values. We use spatial cross-correlation and

regression analyses to show that aesthetic, recreation, economic, spiritual, and therapeutic values

spatially co-locate with special places and thus likely contribute to place attachment. We argue that

survey mapping of landscape values and special places provides a reasonable proxy for scale-based

measures of place attachment while providing richer, place-based information for land use planning.

We conclude by introducing the concept of a map-based place attachment index and suggest that

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0143-6228/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.apgeog.2006.11.002

�Corresponding author. Natural Resource Management, Green Mountain College, Poultney, VT 05764, USA.

E-mail addresses: [email protected] (G. Brown), [email protected] (C. Raymond).

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survey-based measures of landscape values and special places can be used to assess the risk associated

with landscape modification. We provide a map showing one possible place attachment index for the

Otways region and discuss its potential application.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Local values; Participatory GIS; Place attachment; Scale development; Sense of place

Introduction

The emergence of ecosystem management has demanded a new way of valuing naturalresources (Manzo, 2003) which accounts for the values people associate with places orlandscapes (Brown, 2005; Williams & Patterson, 1996), and the personal bonds peopleform with them (Williams & Vaske, 2003). Sense of place has been the focus of studies inthe geographical sciences (Kaltenborn & Williams, 2002) and refers to the attachment oremotional bond people have with place (Altman & Low, 1992; Williams & Stewart, 1998)or the meaning one attributes to such areas (Fishwick & Vining, 1992; Kaltenborn, 1998;Relph, 1976; Stedman, 2003). According to Williams and Vaske (2003), place attachment isthe environmental psychologist’s equivalent of the geographer’s sense of place. When usedbroadly, it refers to the positive emotional bonds that develop between individuals andtheir environment (Altman & Low, 1992; Moore & Graefe, 1994; Williams, Patterson,Roggenbuck, & Watson, 1992).Williams and Vaske (2003) suggest that place bonds can be systematically identified and

measured using a two-dimensional scale of place attachment based on place identity andplace dependence. Place identity refers to the mixture of feelings about specific physicalsettings (Proshansky, Fabian, & Kaminoff, 1983) including how these settings providemeaning and purpose to life (Giuliani & Feldman, 1993; Shamai, 1991; Williams &Roggenbuck, 1989). Place dependence refers to connections based specifically on activitiesthat take place in a setting, reflecting the importance of a place in providing conditions thatsupport an intended use (Schreyer, Jacob, & White, 1981), such as timber harvesting orhorse-riding, as well as the ability for the area to adequately provide for that use (Jacob &Schreyer, 1980). Kyle and colleagues (Kyle, Absher, & Graefe, 2003; Kyle, Graefe,Manning, & Bacon, 2004) have applied both place identity and place dependence measuresand found that they were suitable predictors of resource conflicts, such as attitudestowards fee programs and overcrowding on public lands.While these scales are useful in predicting resource conflicts, there is a need to develop

analytic tools that can address the geographic dimensions of place more directly (Dixon &Durrheim, 2000; Stedman, 2003). Research from the Lake District of Northern Wisconsin(US) demonstrated that landscape attributes were important to constructed meanings, andthat these meanings were not exclusively social (Stedman, 2003). Brown (2005) developed asurvey methodology for measuring and linking place bonds with biophysical landscapecharacteristics by having respondents place coded landscape values on a map of the studyregion. The methodology was modified in more recent trials to include a map-basedmeasure of place attachment that allows survey participants to identify up to six ‘‘specialplace’’ locations in the region of study. Building on work by Brown and others, Black andLiljeblad (2006) developed a methodology for integrating qualitative descriptors of placevalues with spatially referenced ‘‘special area’’ polygons. Passages from transcribed

ARTICLE IN PRESSG. Brown, C. Raymond / Applied Geography 27 (2007) 89–11190

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interviews were coded into social setting, activity, social attachment and biophysical settingcategories and then spatially joined to the special area polygons identified by interviewees.

One unexamined aspect of Brown’s spatial mapping approach is the identification oflandscape values that are the most effective predictors of place identity and placedependence. Can mapped landscape values and special places be used to proxy for scale-based measures of place attachment, and if so, are there advantages to using landscapevalues to measure place attachment?

There are five specific research objectives for this study: (1) to determine if placeidentity and place dependence emerge as distinct constructs in Australia; (2) to examinethe relationships between independent (respondent) variables and place attachment;(3) to determine which landscape values are most predictive of scale-based measuresof place identity and place dependence; (4) to explore the relationship between map-based place attachment (special places) and landscape values; and (5) introduce theconcept of a map-based place attachment index that can be generated from landscapevalue and special place data to aid in the assessment of the risks associated with landscapemodification.

The concept of place

In geography, the concept of place serves as a unit of analysis for integrating natural andsocial science concepts of the environment (Patterson & Williams, 2005; Sack, 1997). Earlyqualitative studies argued that sense of place was dependent on the depth of experiencewith settings (Tuan, 1980) and social relationships with settings (Relph, 1976). Relphdeveloped an ‘insideness’ scale which reflected knowledge of the physical details of place,sense of connection with a community, and a personal connection with place. Onelimitation with these studies is that they have a tendency to emphasize the individualisticdimensions of place (Dixon & Durrheim, 2000), obscuring the collective nature of relationsbetween people, identities, and their environments.

Studies have progressed to place related meanings and the ways in which these meaningscan be applied to ecosystem management (Kaltenborn, 1998; Williams & Patterson, 1996;Williams & Stewart, 1998). Environmental problems affect environmental meaningsincluding inherent (aesthetic) meanings, instrumental (goal-directed) meanings, cultural(symbolic) meanings, and individual (expressive) meanings (Williams & Patterson, 1996).These meanings influence how people perceive environmental conditions and how theyreact to environmental effects (Kaltenborn, 1998).

Although sense of place research recognizes a relationship between environmentalproblems and meanings or values assigned to places, there are no adequate means by whichnatural area planners can undertake a comprehensive and integrated assessment of anindividual’s value for natural areas (Winter & Lockwood, 2004). There have beennumerous attempts to develop scales that enable an integrated assessment, includingvarious place attachment scales (Bricker & Kerstetter, 2000; Moore & Graefe, 1994;Williams & Roggenbuck, 1989); the New Ecological Paradigm (Dunlap, Van Liere,Mertig, & Jones, 2000); and the Natural Area Value Scale (Winter & Lockwood, 2004).The New Ecological Paradigm scale is a measure of environmental or ‘‘ecological’’worldview, encompassing environmental attitudes, beliefs, and values, whereas the NaturalArea Value Scale measures, distinguishes between, and determines the relative strength ofuse, non-use, and intrinsic values for nature to guide decision-making in natural areas.

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Williams and Vaske’s (2003) place attachment scale is one of the first validated scales tosystematically identify and measure meanings (termed place bonds) over a variety of landuse settings. It is based on the place attachment constructs of place identity and placedependence. Kyle and colleagues have applied the place identity and dependenceconstructs to resource management conflicts. Results from a park study in Californiashowed that as place identity increased and the recreationists’ attitude toward the feeprogram became more positive, support for spending fee revenue also increased; however,place dependence contributed little in each of the models examined (Kyle et al., 2003). Incontrast, both place identity and dependence were significant predictors of visitor reactionsto setting densities along an Appalachian Trail (Kyle et al., 2004). Respondents scoringhigh on the place identity dimension were more likely to report feeling crowded; however,respondents scoring high on the place dependence dimension were more inclined to havefavorable attitudes towards the setting.We also recognize other researchers consider place attachment as a separate place

dimension, to be examined alongside place identity and place dependence (Jorgensen &Stedman, 2006; Stedman, 2002, 2003). But for the purposes of this study, we consider placeidentity and place dependence to be integral components of place attachment.

Landscape values and place attachment

Brown (2005) developed a landscape values typology that included a mappingcomponent for soliciting place-based meanings that was applied in five separate surveysof the general public in Alaska (Brown, 2003; Brown & Alessa, 2005; Brown, Reed, &Harris, 2002; Brown, Smith, Alessa, & Kliskey, 2004; Reed & Brown, 2003) and twoseparate surveys in Australia (Brown, 2006; Raymond & Brown, 2006, 2007). In recentversions of the typology, a measure was included allowing the survey participant toidentify and map up to six special places using sticker dots. The ability to identify and maplandscape values and special places is viewed as an operational bridge between sense ofplace and place attachment. Findings from the Chugach National Forest (US) planningstudy suggest that people in communities with strong place attachment are more cohesive,enjoy a higher quality of life, and tend to identify more landscape values and special placesnear their communities (Brown et al., 2002).Black and Liljeblad (2006) mapped place attachment by asking interviewees to locate

‘‘special areas’’ using polygons and to explain why each value was important duringrecorded interviews. This approach seeks to maximize internal validity by focusing on theconceptual foundations of mapped place attachment using a relatively small number of keyinformant interviews (n ¼ 15). This approach is complimentary, but different fromBrown’s method (2005) that uses a predefined set of generalized landscape values andspecial places and regional random sampling procedures to achieve a degree of externalvalidity in mapped place attachment to assess the risks associated with landscapemodification.The place attachment concepts embedded in Brown’s landscape values and special

place approach have not been fully examined. The recent validation of Williams andVaske’s (2003) place attachment scale provides an opportunity to review the relationshipbetween place attachment and the assignment of landscape values and special places. Weincluded their scale items pertaining to place identity and place dependence in a survey ofresidents and visitors to the Otways region, Victoria, to determine potential


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associations between place identity and place dependence constructs and landscape valuemapping.

The inclusion of special place dots in the study survey instrument also provides anopportunity to compare the results of scale-based versus map-based place attachmentmeasures. By map-based measure, we mean the propensity for the survey participant todirectly identify up to six special places in the region. Map-based measures of placeattachment can provide place-specific information about where the landscape bonds exist.If map-based measures of place attachment can provide both generalized place attachmentscores as well as more specific, geographic information about where the intensities of placeattachment exist on the landscape, these measures would offer a distinct advantage in landuse planning applications.

Methods

Study area

The Otways region is located in southwest Victoria, Australia, and covers an area of337,410 ha (VEAC, 2004). We divided the region into the three zones of ‘‘coast’’,‘‘hinterland’’, and ‘‘plains’’ (Fig. 1). The Otway Coast, the region within 5 km of thecoastline, contains a world class driving experience named the Great Ocean Road (GOR).

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Fig. 1. The Otways region of Victoria, Australia was divided into three study zones: coast, hinterland, and plains.

G. Brown, C. Raymond / Applied Geography 27 (2007) 89–111 93

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Over 4.7 million day trips occurred along the GOR in 2002 (Bureau of Tourism Research,2002), placing extreme pressure on physical infrastructure, particularly within the coastaltownships of Lorne and Apollo Bay. Tourism strategies aim to disperse some of the visitoractivity into the largely undeveloped Otway Hinterland (DSE, 2005).The Otway Hinterland broadly contains the mountains and the forests up to 30 km from

the coast. Some of the largest trees in the world named Eucalyptus regnans inhabit thisarea. Historically, much of the Otway Hinterland was under state forest ownership andopen to clearfelling; however, recent public opposition to the practice led to a commitmentby the Victorian Government to double the total area of permanent conservation reserve inthe Otways from 49,355 to 101,960 ha. The expanded conservation reserve has beengazetted as the Great Otway National Park in the National Parks (Otways and Other

Amendments) Act (Vic) 2005. The remaining state forest, recently renamed the OtwayForest Park, is designated for recreational use and minor extractive industries, such asharvesting of tea-tree for garden stakes. Broad scale logging of native forests will cease by2008.The remaining low relief lands to the north of the Otway Hinterland are the Otway

Plains. This area is the most densely populated region and contains the regional hub ofColac with a strong retail and commercial economy.

Sampling

The study consisted of two phases. Phase one consisted of a survey administered to1400 residents of the Otways region of Victoria. We collected names and addressesthrough a systematic random sample of the 2003 electoral roll for the division ofCorangamite, Victoria (Australian Electoral Commission, 2003). We then separated thesample into coastal, hinterland, and plains residents according to the regional boundariesidentified in Fig. 1. Phase two consisted of a survey administered to 500 visitors,convenience-sampled at the entrance to a tree-top canopy walk named the Otway Fly anda rainforest walk named Maits Rest. Visitors were also sampled in the coastal township ofLorne.

Instrument

Part 1 of the survey instrument consisted of 15 place attachment statements, taken fromstudies that previously demonstrated good internal consistency (Jorgensen & Stedman,2001; Williams & Vaske, 2003). We used six scale items to represent place identity and fiveitems to represent place dependence. The scale items were preceded by the followingdirections: ‘‘Below are a set of statements about your attachment to the Otways region.Please indicate your level of agreement or disagreement with each statement’’. Items werepresented on a 5-point Likert scale where ‘‘1—Strongly Agree’’, ‘‘5—Strongly Disagree’’,and ‘‘3—Neither Agree or Disagree’’.Part 6 of the survey solicited landscape values and special places for the Otways region.

We first asked survey participants to place mnemonically coded sticker dots describing 12landscape values on a 1:125,000 greyscale map of the Otways region (Spatial Vision, 2001).The question was worded: ‘‘Find the enclosed Otway map and set of sticker dots. There are12 sets of dots that identify different values for places in the Otways such as scenic value orrecreation value. Stick the dots on the Otway map where you think these values are. These


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dots also have ‘importance’ ratings from 5 to 50 points. Put the largest scenic dot (e.g., 50a)on the most scenic places, the largest recreation dots (50r) on places with the highestrecreation value, and so on with the other value dots. Use as many or few dots as you like.’’Each value was accompanied by a short phrase to communicate its intended meaning(Fig. 2). Respondents were also asked to identify six ‘‘special places’’ in the Otways regionusing sticker dots coded from ‘‘P1’’ to ‘‘P6’’ and to explain why each of these places werespecial to them.

The survey also contained several questions that provide an opportunity to explorethe criterion-related (predictive) validity of the place attachment scale—the extent towhich the scale results are consistent with place attachment concepts. Respondentswere asked to identify their length of residence, knowledge of the Otways region, andwhether they saw themselves as an advocate for the environment. Questions included:‘‘About how long have you lived in the Otways region?’’ and ‘‘How would you rate yourknowledge of places in the Otways?’’ The knowledge question was presented on a scalewhere ‘‘1—Excellent’’, ‘‘2—Good’’, ‘‘3—Fair’’, and ‘‘4—Poor’’. The advocacy questionasked respondents: ‘‘Do you consider yourself an advocate for the environment?’’ on a‘‘Yes’’, ‘‘No’’, or ‘‘Somewhat’’ scale. Based on place attachment concepts, one wouldexpect individuals with greater knowledge of the region, who have lived in the Otwayslonger, and with greater concern for the environment to show higher levels of placeattachment.

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Fig. 2. Mnemonic sticker dots and landscape value legend used in Otways region survey.


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Analyses

To determine potential sample biases in the Otways sample, we compared respondentsocio-demographic characteristics such as age and education level with Australian Bureauof Statistics (ABS) data. We also examined the proportional differences between residentand visitor perceived knowledge of the Otways and their level of advocacy for theenvironment. Responses were grouped by resident, visitor, and subregion of residence(coast, hinterland, and plains).We ranked the mean landscape value importance scores to understand what values were

most important to residents and visitors. Each of the 12 landscape values had sixmnemonically sticker dots with varying importance ratings ranging from 5 to 50 points,with the total number of points equalling 100 (see Fig. 2). Survey participants could placeany combination of landscape value sticker dots on the enclosed Otways map to indicateboth the location and the importance of the value. Thus, importance scores for eachlandscape value could range from 0 to 100 depending on the number of dots placed on themap. To further examine value importance rankings, we coded the special placedescriptions into the 12 landscape values, as well as two recurring themes of ‘‘home’’(the place where I live) and ‘‘family connection’’ (a place where I have met family). Thecoded special place descriptions were then ranked based on frequency of responsecategory.We used principal components analysis with Varimax rotation in SPSSs (Version 11.0)

to determine the number of linear components that provide a good fit for the latentconstructs of place identity and place dependence (factor validity). Principal componentanalysis is a type of exploratory factor analysis that explains the maximum amount ofcommon variance in a correlation matrix using the smallest number of explanatory factors(Field, 2000). The greatest variance by any projection of the data comes to lie on the firstcoordinate (called the first principal component), the second greatest variance on thesecond coordinate, and so on.To examine the criterion-related validity of the place attachment scale, we ran bivariate

correlations between place identity and dependence summative scales and respondentvariables of length of residence, knowledge, and advocacy for the environment. We alsoran bivariate correlations between the map-based measure of place attachment and theserespondent variables.To explore the relationship between landscape values and place attachment, we used

two different measures of place attachment—the scale-based measures previouslydescribed and a map-based measure of place attachment. The map-based measure ofplace attachment is a score ranging from 0 to 6 representing the number of optional‘‘special places’’ mapped by the survey participant. We believe the propensity forthe respondent to identify and map special places in the region is a type of prima facia

place attachment measure. For this supposition to hold, one would expect to find asignificant correlation between the place attachment scale and the number of special placesmapped. Before proceeding with further analyses, we examined this relationship. Thebivariate correlation between the place attachment scale score (identity and dependencecombined) and the map-based score was modest, but statistically significant (r ¼ 0:27,po0:001).We examined the relationship between landscape values and both the scale and map-

based measures of place attachment using multiple regression analysis. In the regression


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model using the place attachment scale, the landscape value importance scores(range 0–100) for the combined resident and visitor groups were treated asindependent variables and the mean scale scores for place identity and depen-dence were treated as dependent variables. We used the ‘‘enter’’ method toforce all landscape values into the regression model and included the variance inflationfactor (VIF) collinearity diagnostics to assess the extent to which predictor variableswere interrelated. To examine the relationship between landscape values and themap-based measure of place attachment, we regressed the 12 landscape value importancescores (range 0–100) against the number of special places identified by respondents (range0–6).

The final analysis examined the spatial relationship between mapped specialplaces and landscape values using spatial cross-correlation and multiple regressionanalyses. The analyses were accomplished by generating continuous density surfacesfrom the landscape value and special place point locations in the study area usinga kernel density function and then regressing landscape value densities against specialplace density using a sample of randomly generated locations. To conduct thespatial analysis, some preliminary data preparation was required. Each of the 12landscape values and special place point distributions were converted to raster data(grids) in ArcView Spatial Analysts by calculating the density of point loca-tions using a consistent density criteria (500m grid cell, 2000m search radius).Each grid was then clipped to the study area polygon resulting in 20,473 grid cells foranalysis.

Each grid cell represents three values (x, y, z) with x and y denoting unique spatialcoordinates (latitude and longitude) and z denoting the calculated landscape value orspecial place point density. Thus, a given grid cell would have 12 separate landscape valuedensities and one special place density associated with it. The 13 density grids wereexported as (x, y, z) data and imported into SPSSs for correlation and regression analyses.

Spatial cross-correlation coefficients were computed from a set of 1000 randomlylocated points between landscape value and special place surfaces to describe the extent ofspatial co-location between the special place and landscape value locations. The use of a setof random points rather than the entire grid surface avoids the potential confoundingeffects of spatial autocorrelation.

Regression analysis was used to determine the relative strength of the predictorvariables (landscape value locations) in determining the locations of map-basedplace attachment (special places). With the special place density variable as thedependent variable, multiple regressions was performed with the 12 landscape valuedensities as independent variables. Lacking sound theoretical reasons for includingor excluding predictors in the regression model, the ‘‘stepwise’’ method of regression waschosen to select predictors based on a purely mathematical criterion. The primarymethodological concern is with the expected collinearity that can influence the importanceof predictor variables shown by the model’s standardized beta coefficients. In theabsence of serious collinearity problems, the larger the absolute value of the standardizedbeta coefficients, the stronger the spatial association of the landscape value to the specialplaces.

The final step in the analysis was to visually display the density of mapped special placelocations in the Otways region using a standardized density scale ranging from 0 to 1 toserve as a prospective place attachment index.


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Results

Socio-demographic characteristics

Resident respondents were older and had completed a higher level of education (37.3%with tertiary or postgraduate degree) than would be expected based on comparable ABSstatistics. The proportions of male and female respondents were similar to ABS data. Theviews expressed in the survey appear credible based on the significant collective experienceand knowledge of the respondents. The majority of residents indicated good (57.5%) orfair knowledge (24.6%) of places in the Otways (Table 1). Visitors indicated lessknowledge of places in the Otways than residents, with the majority expressing good(34.3%) or fair (45.5%) knowledge of the Otways.There were significant differences in the length of residence by subregion. Overall, most

resident respondents have lived in the Otways for 11–30 years (44.3%), but proportio-nately more plains residents (45.6%) than coastal residents (22.1%) have lived in theOtways for greater than 30 years (X 2 ¼ 38:77, po0:001).The majority of resident (50.6%) and visitor (51.9%) respondents were self-reported

advocates for the environment with one significant difference based on subregion ofresidence. Plains residents (44.4%) were less likely to consider themselves environmentaladvocates than hinterland residents (58.7%).In summary, respondent characteristics indicate some bias toward older, more educated,

and knowledgeable Otways residents. While sample deviations from true populationcharacteristics are generally viewed negatively, this bias can be placed in a positive light

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Table 1

Otways region respondent characteristics

Characteristic n Residents

(%)

Visitors

(%)

X2 Coastal

residents

(%)

Hinterland

residents

(%)

Plains

residents

(%)

X2

Knowledge of Otways region

Excellent 93 15.0 5.2 8.2 21.7 16.7

Good 388 57.5 34.3 57.7 63.3 54.3

Fair 232 24.6 45.5 85.87*** 30.2 15.0 25.3 22.11***

Poor 48 2.9 15.0 3.9 0.0 3.7

Total 761 100.0 100.0 100.0 100.0 100.0

Length of residence

p10 yr 114 21.0 n/a 33.1 19.0 12.9

11–30 yr 241 44.3 n/a 44.8 49.6 41.5 38.77***

430 yr 189 34.7 n/a 22.1 31.4 45.6

Total 544 100.0 100.0 100.0 100.0

Advocate for the environment

Yes 390 50.6 51.9 51.1 58.7 44.4

No 78 11.6 6.5 4.52 10.3 6.6 15.5 9.85*

Somewhat 297 37.7 41.6 38.5 34.7 40.2

Total 765 100.0 100.0 100.0 100.0 100.0

Note: ***po0:001, **po0:01, *po0:05

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because greater knowledge and experience with the Otways is likely to have increased thereliability of the mapping component of the survey.

Relative importance of landscape values

Table 2 shows the mean importance scores for the 12 landscape values in the study. Bothresidents and visitors perceived aesthetic and recreation values to be most important andspiritual and intrinsic values to be least important. Residents perceived economic values(rank ¼ 3) to be more important than visitors (rank ¼ 8), but visitors assigned higherrelative importance to future values (rank ¼ 3). There are minor variations in landscapevalue importance by subregion of residence. Hinterland residents assigned higher relativeimportance to biological diversity values (rank ¼ 5) than coastal residents (rank ¼ 6) andplains residents (rank ¼ 7).

We found an association between the number of value dots assigned to the map andvalue importance scores. With the exception of aesthetic values, visitors placedproportionately fewer value dots on the Otways map resulting in generally lower meanimportance scores. The low intrinsic and spiritual importance scores for both residents andvisitors may reflect difficulty in assigning intangible qualities to landscape features. Thisfinding is supported by the frequency of descriptions that accompanied special placedesignations (Table 3). Respondents infrequently identified spiritual value as a specialplace description (n ¼ 11) and more commonly referred to special places as areasassociated with aesthetic, recreation, or therapeutic value.

Generalisability of place identity and place dependence constructs in Australia

In the Varimax rotated model, resident factor loadings for the place identity dimensionranged from 0.61 to 0.86, and visitor responses ranged from 0.70 to 0.87 (Table 4).The factor loadings were similar for coastal, hinterland, and plains residents (range ¼0.56–0.89).

All items in the place dependence dimension followed a pattern similar to place identity.For residents, factor loadings ranged from 0.67 to 0.87 and for visitors 0.66–0.83. Again,the factor loadings were similar for coastal, hinterland, and plains residents (range ¼0.61–0.88).

Overall, the exploratory factor analysis demonstrated an acceptable fit for the placeidentity and dependence dimensions for Otways region residents and visitors. HighCronbach’s alpha (reliability analysis) scores indicate that both place identity (six items)and dependence (five items) constitute cohesive subscales and contribute significantly tothe reliability of the overall place attachment scale (11 items, Cronbach’s alpha X0.92) forall analysis groups.

Relationships between place attachment and respondent variables

We first ran a three-way full factorial model to determine potential interaction effectsbetween the three respondent variables and place identity and dependence; however, nointeractions were statistically significant. The knowledge, length of residence, andadvocacy for environment variables explained 23% of the variance in place identity and10% of the variance in place dependence.


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Table

2

Relativelandscapevalueim

portance

asperceived

byresidents

andvisitors

Residents

Visitors

Value

na

Mean

importance

bOverall

resident

rankc

Coast

rank

Hinter-land

rank

Plains

rank

Value

nMean

importance

Visitor

rank

Aesthetic

1780

78.15

11

11

Aesthetic

714

78.11

1

Recreation

1557

70.75

22

22

Recreation

517

59.49

2

Economic

1393

64.98

33

33

Future

378

46.65

3

Wilderness

1202

57.48

44

45

Heritage

345

46.35

4

Heritage

1163

55.44

57

64

Wilderness

367

45.97

5

Lifesustaining

1134

54.33

65

76

Biologicaldiversity

369

44.35

6

Biologicaldiversity

1128

53.98

76

57

Therapeutic

369

44.24

7

Future

1051

50.20

89

88

Economic

378

43.86

8

Knowledge

1023

49.51

98

99

Lifesustaining

324

39.05

9

Therapeutic

963

45.75

10

11

10

10

Intrinsic

311

38.32

10

Intrinsic

924

44.20

11

10

12

11

Knowledge

307

37.84

11

Spiritual

802

39.87

12

12

11

12

Spiritual

241

31.46

12

Total

14,120

Total

4620

aThetotalnumber

ofvaluedots

placedontheOtw

ayslandscapebyOtw

aysresidents

orvisitors.

bThemeanresidentlandscapevalueim

portance

score.Scoresrangefrom

0to

100dependingonthenumber

ofdots

placedonthemap.

cLandscapevalues

are

ranked

basedonmeanim

portance.


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pyWe ran bivariate correlations to determine the strength of the relationship between

each respondent variable and place identity and dependence (Table 5). For allanalysis subgroups, respondents who expressed more knowledge of the Otwaysregion had significantly higher place identity and place dependence than thosewho expressed less knowledge of the region (rX0:175, po0:05). These relationships arestronger for place identity than place dependence, with the exception of hinterlandresidents.

We found weak but significant, positive relationships between length of residence andplace identity (r ¼ 0:144, po0:05), and advocacy for the environment and place identity(r ¼ 0:328, po0:001), with the exception of hinterland residents (rp0:133, p40:05). Placedependence showed weaker, but still significant relationships with length of residence andenvironmental advocacy.

We also ran bivariate correlations between the map-based measure of placeattachment (range 0–6) and the respondent variables of length of residence, knowledgeof the Otways region, and advocacy for the environment. There was no significantrelationship between length of residence and map-based place attachment (r ¼ 0:072,p40:05), but there were significant relationships between knowledge of the Otways region(r ¼ 0:250, po0:01) and advocacy for the environment (r ¼ 0:115, po0:01) for all residentsubgroups and visitors.

Overall, these findings augment the external validity of the place identity and placedependence scales for use in Australia. The scale-based measure of place attachment issignificantly, but moderately, related to knowledge of the Otways region, length ofresidence, and advocacy for the environment. Respondent variables generally havestronger relationships with place identity than place dependence with some exceptionsamong hinterland subgroups. These respondent variables have weaker, but consistentrelationships with the map-based measure of place attachment.

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Table 3

Frequency and type of values associated with special place designations

Value na Rank

Aesthetic 303 1

Recreation 281 2

Therapeutic 102 3

Biological diversity 100 4

Wilderness 99 5

Homeb 96 6

Heritage 82 7

Family connectionc 51 8

Intrinsic 42 9

Economic 30 10

Spiritual 12 11

Life sustaining 8 12

Learning 2 13

Future 1 14

aThe total number of value dots placed on the Otways landscape by Otways residents.bHome refers to ‘‘I live here’’.cFamily connection refers to a special place for family meetings.


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Regression models were used to help identify the landscape values that best predict thescale and map-based measures of place attachment as determined by the magnitude of thestandardized beta coefficients. The results from regressing landscape value importancescores against the place identity and place dependence scales appear in Table 6. Althoughthe results of both regression models were statistically significant (po0:05), landscapevalues explain a relatively small amount of the variance in place identity and placedependence (R2 ¼ 0:18 and 0.12, respectively). Three landscape values were significantpredictors of both place identity and place dependence: spiritual value (b ¼ 0:314,b ¼ 0:258, po0:001), wilderness value (b ¼ 0:171, b ¼ 0:160, po0:01), and aesthetic value(b ¼ �0:118, b ¼ �0:125, po0:01). Future value was an additional significant predictor ofplace identity (b ¼ �0:165, po0:01) and intrinsic value was an additional significantpredictor of place dependence (b ¼ �0:156, po0:05).The collinearity diagnostics on the regression models suggest a tolerable level of

multicollinearity in the independent variables. The diagnostics in both models show VIFvalues ranging from 1.3 to 2.7, below the threshold of 10 for obvious concern (Myers,1990). Analysis of the regression residuals does not provide sufficient evidence to reject the

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Table 4

Exploratory factor analyses for place identity and place dependence items for residents and visitors, including

resident subregions

Place attachment items Residents

(n ¼ 522)

Visitors

(n ¼ 199)

Coastal

residents

(n ¼ 168)

Hinterland

residents

(n ¼ 116)

Plains

residents

(n ¼ 238)

Place identity

I feel the Otways are a part of me 0.78 0.78 0.83 0.75 0.76

The Otways are very special to me 0.86 0.84 0.86 0.86 0.86

I identify strongly with the Otways 0.86 0.87 0.85 0.82 0.89

I am very attached to the Otways 0.85 0.86 0.84 0.79 0.88

Living in the Otways says a lot about who I am 0.61 0.70 0.56 0.66 0.60b

The Otways mean a lot to me 0.84 0.85 0.85 0.86 0.83

Cronbach’s alpha (place identity) 0.93 0.93 0.92 0.92 0.94

Place dependence

The Otways are the best place for what I like to

do

0.67 0.66 0.73 0.61 0.64

No other place can compare to the Otways 0.79 0.79 0.81 0.73 0.78

I get more satisfaction out of living in the

Otways than any other place

0.76 0.83 0.76 0.78 0.77

Doing what I do in the Otways is more

important to me than doing it in any other

place

0.85 0.82 0.83 0.83 0.87

I would not substitute any other area for doing

the types of things that I do in the Otways

0.87 0.83 0.86 0.88 0.87

Cronbach’s alpha (place dependence) 0.90 0.89 0.89 0.89 0.90

Cronbach’s alpha (all items) 0.94 0.92 0.92 0.93 0.94

Note: All factor loadings are significant for the corresponding sample size.


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Table

5

Relationshipsbetweenplace

attachmentandrespondentvariablesfordifferentsubregionsofresidence.

Residents

Visitors

Coastalresidents

Hinterlandresidents

Plainsresidents

Variable

Place

identity

Place

dependence

Place

identity

Place

dependence

Place

identity

Place

dependence

Place

identity

Place

dependence

Place

identity

Place

dependence

Length

ofresidence

(0–100yr)

Pearsoncorrelation

r0.144*

0.110*

n/a

n/a

0.317***

0.218**

0.133

0.208*

0.143*

0.047

KnowledgeofOtw

ays(1—Poor,2—Fair,3—Good,4—Excellent)

Spearm

ancorrelation

r0.398***

0.253***

0.481***

0.329*

0.371***

0.175*

0.213*

0.227*

0.496***

0.287***

Advocate

fortheenvironment(1—No,2—Somew

hat,3—Yes)

Spearm

ancorrelation

r0.328***

0.163**

0.291***

0.150*

0.278*

0.027

0.130

�0.042

0.419***

0.291***

Note:***po0:001,**po0:01,*po0:05

n/a

denotesnotapplicable

forvisitors

Place

identity

andplace

dependence

item

swerebasedonascale

rangingfrom

1—Strongly

Agree,

2—Agree,

3—Neither

AgreeNorDisagree,

4—Disagree,

5—

Strongly

Disagree.


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pyhypothesis that the residuals in both the place identity and dependence models arenormally distributed using the Kolmogorov–Smirnov test for normality (KS ¼ 0.033 and0.036, p40:05).The relationship between landscape value importance scores and the map-based

measure of place attachment was explored by treating the landscape value importancevariables as independent variables and the number of special places identified (range 0 to 6)as the dependent variable. The results of the regression analysis appear in Table 7. Theoverall model fit is relatively poor but statistically significant (R2 ¼ 0:19, po0:001). Twolandscape values were significant predictors of special place value designation: wildernessvalue (b ¼ 0:204, po0:001) and spiritual value (b ¼ 0:130, po0:05). The collinearitydiagnostics on this regression model also suggest a tolerable level of multicollinearity in theindependent variables.

Spatial relationship between map-based place attachment and landscape values

We examined the spatial relationship between map-based place attachment and mappedlandscape values using spatial cross-correlation and multiple regression analyses. Thespatial cross-correlation coefficients between the densities of the 12 landscape values andspecial places appear in Table 8. The spatial cross-correlation coefficients from a previousstudy in Kangaroo Island, South Australia (Brown, 2006) are provided in the table forcomparison. The largest correlation coefficients are associated with aesthetic (r ¼ 0:92),recreation (r ¼ 0:94), spiritual (r ¼ 0:90), and therapeutic (r ¼ 0:87) landscape values.Although the correlation coefficients are larger for most landscape values in the Otwaysstudy, the relative magnitude of the correlation coefficients appear consistent with the

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Table 6

Linear regression results for landscape values regressed against place identity and place dependence scale-based

measures

Model results Dependent variable: place identity scale Dependent variable: place dependence scale

R R2 F p R R2 F p

0.420 0.18 10.19 0.000 0.352 0.12 6.69 0.000

Independent variables b t Tolerance VIF b T Tolerance VIF

Aesthetic �0.118 �2.712** 0.775 1.290 �0.125 �2.795** 0.775 1.291

Economic 0.092 �1.900 0.624 1.602 0.081 1.644 0.630 1.586

Recreation 0.024 0.482 0.597 1.675 0.033 0.649 0.597 1.675

Life sustaining 0.036 0.619 0.432 2.314 0.004 0.071 0.434 2.304

Knowledge 0.051 0.939 0.374 2.674 0.126 1.956 0.374 2.676

Biological diversity 0.020 0.326 0.410 2.438 0.006 0.102 .419 2.388

Spiritual 0.314 5.945*** 0.531 1.882 0.258 4.764*** 0.529 1.891

Intrinsic �0.086 �1.459 0.427 2.344 �0.156 �2.568* 0.421 2.376

Heritage 0.078 1.386 0.465 2.150 0.073 1.268 0.462 2.166

Future �0.165 �2.832** 0.433 2.310 �0.108 �1.793 0.425 2.354

Therapeutic �0.033 �0.557 0.412 2.426 �0.079 �1.281 0.410 2.438

Wilderness 0.171 3.012** 0.460 2.172 0.160 2.752** 0.457 2.189

Note: Bold indicates variable is statistically significant as follows: ***po0:001, **po0:01, *po0:05, b—standardized coefficients. Dependent variables reflect the mean place identity and place dependence scores of

Otways region residents and visitors combined.


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results from Kangaroo Island; aesthetic, recreation, spiritual, and therapeutic values alsohave the largest correlation coefficients in the Kangaroo Island study.

In the regression of landscape value densities against the densities of map-based placeattachment (special places), 7 of the 12 landscape predictor variables emerged asstatistically significant predictor variables using stepwise regression (see Table 9). In thismodel, these landscape value variables were strongly predictive of the location of specialplaces (R2 ¼ 0:97, po0:001). The standardized beta coefficients of the predictor variables

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Table 8

Spatial cross-correlations between landscape value locations and special place locations for Otways regional study

(Victoria) and study of Kangaroo Island residents, South Australia (see Brown, 2006)

Landscape value Otways region study (n ¼ 1000) Kangaroo Island study (n ¼ 1000)

Aesthetic 0.92 0.88

Economic 0.84 0.47

Recreation 0.94 0.86

Life sustaining 0.41 0.21

Knowledge 0.81 0.49

Biological diversity 0.76 0.44

Spiritual 0.90 0.80

Intrinsic 0.80 0.83

Heritage 0.58 0.45

Future 0.82 0.61

Therapeutic 0.87 0.90

Wilderness 0.47 0.35

Table 7

Linear regression results for landscape values regressed against number of special places identified by Otways

region residents and visitors

Model results Dependent variable: frequency of special place identification

R R2 F P

0.438 0.19 11.755 0.000

Independent variables b t Tolerance VIF

Aesthetic �0.040 �0.962 0.783 1.28

Economic 0.071 1.526 0.631 1.58

Recreation 0.037 0.781 0.600 1.67

Life sustaining 0.042 0.751 0.437 2.29

Knowledge 0.060 1.012 0.390 2.56

Biological diversity �0.046 �0.808 0.424 2.36

Spiritual 0.130 2.563* 0.527 1.90

Intrinsic 0.006 0.097 0.423 2.37

Heritage �0.015 �0.278 0.457 2.19

Future 0.035 0.627 0.430 2.33

Therapeutic 0.059 1.034 0.422 2.37

Wilderness 0.204 3.766*** 0.463 2.16

Note: Bold indicates variable is statistically significant as follows: ***po0:001, **po0:01, *po0:05, b—standardized coefficients.


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indicate the relative strength and direction of relationship between landscape valuelocations and special place locations. The most significant predictor variables wererecreation (b ¼ 0:349), aesthetic (b ¼ 0:348), economic (b ¼ 0:168), and spiritual(b ¼ 0:135) values, all of which were positively associated with special place locations.Unlike the previous regression analysis, multicollinearity among the independent variablesis a concern and the regression analysis of multiple trials (n ¼ 1000) yield variable resultsbeyond the first two predictor variables. Recreation and aesthetic value locations areconsistently the strongest predictors of special place locations in the regression models,being stronger by a factor of at least two over all the other variables. In multiple regressiontrials (n ¼ 1000) or with the inclusion of all landscape value densities in the analysis(n ¼ 20; 437), economic and spiritual, landscape value densities emerge as the mostsignificant predictor variables following recreation and aesthetic values. The importance ofrecreation and aesthetic landscape values as key determinants of special place location isconsistent with the findings from the open coding of special place descriptions (Table 3).To illustrate how map-based measures of place attachment might be used for land use

planning purposes, a map of special place location densities was generated andstandardized on a scale of 0–1 for the Otways region (Fig. 3). The actual special placepoint locations used to derive the index also appear on the map. This special place indexvisually depicts geographic locations with variable levels of place significance to residentsand visitors. The greatest concentration of special places is distributed primarily along thecoast, with high densities near the coastal townships of Lorne and Apollo Bay andmoderate intensities within the Cape Otway region (a popular tourist attraction). Lowintensities of special places occur along the remaining coastal strip.

Discussion

The objectives of this study were to examine the relationships between place attachmentand landscape values using two measures of place attachment—a psychometric, scale-based measure (Williams & Vaske, 2003) and a map-based measure derived from survey

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Table 9

Linear regression results for landscape values densities regressed against special place location densities

Model results Dependent variable: density of special place locations

R R2 F p

0.984 0.97 4454.68 0.000

Independent variables Standardized coefficients T Tolerance VIF

Recreation 0.349 21.592 0.119 8.38

Aesthetic 0.348 22.733 0.133 7.54

Economic 0.168 13.543 0.202 4.94

Spiritual 0.135 10.183 0.177 5.65

Life sustaining 0.042 5.311 0.508 1.96

Biological 0.030 2.626 0.231 4.33

Knowledge 0.066 5.196 0.192 5.19

All independent variables are significant predictors of special place locations (po0:001).


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pyresearch (Brown, 2005). Exploratory factor analysis reveals that the place attachment scaleseparates into two principal components with high loadings supporting the findings ofWilliams and Vaske (2003). Visitors recorded similar factor loadings on each of the placeidentity and dependence items as residents, suggesting that the concept of ‘‘home’’ or‘‘place’’ extends beyond one’s place of residence to places visited. Traditionally, residentattitudes toward place have been an area of focus in the tourism planning literature(Harrill, 2004), but our results indicate comparable levels of place attachment such thatboth residents and visitors should be included in place-based tourism research.

The place dependence dimension had smaller factor loadings per item compared to placeidentity, consistent with the results of Williams and Vaske (2003). These researchersattributed the greater variance to the negatively worded question of ‘‘The things I do at ‘X’I would enjoy doing just as much at a similar site’’ (p. 835); however, this question wasomitted from the Otways survey, suggesting other factors may be leading to the differencebetween place identity and place dependence.

We did not expect the variance in place attachment in the psychometric scales to be fullycaptured by the finite list of respondent mapped landscape values. Indeed, participantmapping of landscape values explains a relatively small amount of the total variance in theplace attachment constructs of identity and dependence. But the regression analyses dididentify the landscape values that are most predictive of scale-based place attachment—respondents’ perceptions of spiritual, wilderness (naturalness), and aesthetic connections

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Fig. 3. Density map of special places in the Otways region of Victoria based on 500m grid cell and 2 km search

radius. Densities are indexed on 0–1 scale with index values closest to one reflecting greater risk to resident and

visitor place attachment from landscape modification.


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to the landscape. These predictor variables are consistent with findings from Kaltenbornand Bjerke (2002) who found strong associations between place attachment and attractive,natural landscapes in southern Norway.Given the success in measuring place attachment using psychometric scales, we sought

to determine whether place attachment could be measured using mapped special placesrather than psychometric scales. The scale-based measures of place attachment aregeneralized expressions derived from a multitude of cognitive, affective, and behaviouralexperiences by residents and visitors to the region. In contrast, respondent mapped specialplaces represent place-based expressions of value based on a combination of place-basedexperiences and preferences as well as place-based symbolic expressions that are notnecessarily derived from experiences (e.g., the spiritual and intrinsic special placedescriptions in Table 3).When the landscape values were regressed against the number of special places mapped,

the map-based measure of place attachment, wilderness and spiritual values werestatistically significant predictor variables. The importance of spiritual value in both thescale and map-based regression analyses, despite its relatively low frequency of mapplacement by participants, merits further supposition.We posit that an abundance of aesthetic and wilderness/natural landscape features,

along with recreation and therapeutic experiences in those landscapes, help create theconditions that lead to place attachment, but it is the individual’s willingness to associatespiritual value with a landscape that best predicts the psychological state of placeattachment. Although respondents are least likely to identify spiritual landscape values,these mapped landscape values are most important in identifying respondent attachmentto place.The spatial analysis of landscape value densities with the map-based measure of place

attachment (special places) confirms the importance of human engagement with alandscape in developing some level of place attachment. The significant spatial co-locationof recreation and aesthetic values with special place locations, and to a lesser extent,economic and therapeutic values, reflect the transactive nature of human–landscapeinteractions (Brown, 2005; Zube, 1987) where humans are active participants in thelandscape—thinking, feeling, and acting—leading to the attribution of meaning and thevaluing of specific landscapes and places. The place attachment construct is complexwherein individual, socio-cultural, and intrinsic landscape attributes contribute to itsformation. The research presented herein does not attempt to parse the relativecontribution of each domain, a research objective that remains challenging and illusive,but rather seeks to engage the place attachment construct in its ‘gestalt’ form for land useplanning considerations.From a pragmatic, land use planning perspective, map-based measures of place

attachment offer an advantage over scale-based measures because of their place-specificattributes. The mapping of landscape values and special places can provide an operationalbridge between place attachment and applied land use planning that seeks to minimizepotential land use conflict. Learning that residents or visitors have regional placeattachment through psychometric, scale-based measures may alert planning authorities tothe general risk of introducing landscape changes, but map-based measures of placeattachment offer a more specific, placed-based assessment of risk. With respect to theOtways, special place locations are concentrated along the coast, the most active region ofcurrent and prospective tourism and residential development (Fig. 3). These areas would


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appear to pose the greatest challenge to sustaining resident and visitor place attachment inthe presence of high potential for landscape modification.

One of the persistent critiques of sense-of-place research in natural resource manage-ment has been the inability to translate research findings into rational managementdecision models. How does one specifically manage a landscape for sense of place or placeattachment? Part of the shortcoming has been the inability to translate place attachmentmeasures into much more than a list of place names or a generalized scale-based measureof place attachment.

We believe these study results suggest a potential pathway to improve land use decision-making by assessing and mapping the potential risk to resident or visitor place attachment.Maps of place attachment indices can be generated from mapped landscape values andspecial places to identify locations with higher and lower relative place attachment scores.The density map of special places indexed between 0 and 1 presented in this paperrepresents one of several possible place attachment map indices. Other potential indicesmight include maps that combine several landscape values such as aesthetic, recreation,and spiritual landscape values, values that predict special place locations in this study.

Maps that display indices of relative place attachment from landscape values and specialplaces do not provide a set of decision rules regarding land use, nor provide a set ofprescriptive land use options. However, map-based measures of place attachment canprovide commensurable risk indices associated with potential land use change. These placeattachment index maps can graphically display the potential negative consequences ofintroducing land use change. For example, commercial development along coastal strips inthe Otways region will likely reduce the aggregate regional measure of place attachment.

Of course, land use decisions must necessarily be context specific and will involve manymore decision variables than simply a hypothetical risk to place attachment. Further, somemight argue that the mapping of landscape values and special places and their useto generate indices of risk to place attachment are biased toward land use preservationand can rightfully point to human modified landscapes that actually create or strengthenplace attachment. We agree, which is why we advocate the inclusion of mappingmethods that include spatial preferences for development and non-development (seeBrown, 2006; Raymond & Brown, 2007). A place attachment risk index could be adjustedby overlaying development preferences that include spatial measures of land use changewith assent.

While we have demonstrated in this paper that psychometric scales of place attachmentrepresent valid and reliable measures in Australia, we find less practical advantage to theirutilization in regional planning studies when the mapping of landscape values and specialplaces can also provide reasonable measures of resident and visitor place attachment, whilesimultaneously providing a much richer set of analytical tools for assessing theconsequences of potential land use change. Further research is needed to assess the utilityof potential place attachment risk indices in the context of specific, proposal land usedecisions.

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article in press - landscapevalues.org · react to environmental effects (kaltenborn, 1998)....

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